With more and more data available for analysis and
decision making - from web documents and digital media to sensory data
from cameras, microphones, and ubiquitous devices - it becomes
increasingly more important to understand how such large volumes of data
can be analyzed by computers and used as the basis for new intelligent
services, for decision making, and for making computers learn from
experience. In companies around the world, from retail and banks all the
way to Google, intelligent learning and analysis techniques are used to
improve business decisions. Likewise, in science, important discoveries
are made easier by automated learning methods, and games and other
artifacts are being made adaptive with learning technology.Within
the intelligent systems track of the computer science Master's program,
intelligent learning and analysis systems are one of the two major
topics. This module (Machine Learning) is one of the two modules that
are offered as an introduction for master's students to Intelligent
Learning and Analysis Systems. The other is the Data Mining module
taught in the summer semester. Both modules can be selected in either
order, and you may choose to attend one or both of them. For a complete
introduction to the topic, it is recommended to attend both modules.In
the Machine Learning module in particular, we will give a practically
oriented introduction into the most popular methods from Machine
Learning as a subfield of Intelligent Learning and Analysis Systems. We
will get to know decision tree methods, instance-based learning,
artificial neural networks, probabilistic learning, regression methods,
kernel methods and support vector machines, and reinforcement learning
for intelligent agents. This will be complemented with lectures on the
most important approaches within computational learning theory. Within
the exercises, it is possible to try out the most important methods and
popular Machine Learning systems.

Details

Traditional machine learning and data mining
algorithms are resorted to data that can be represented by a single
table of fixed width; the rows and the columns correspond to objects and
object attributes, respectively. This assumption turns out to be quite
restrictive in numerous practical applications involving structured
data, such as graphs or relational structures. The lecture will cover
the basics of learning and mining graph structured data and relational
structures. We will present various algorithms for this setting and
analyse their computational properties. We will also discuss some
interesting applications in bioinformatics, computational chemistry, and
natural language processing.